INNER CODE UNIT · Python

neg_num

nv-tlabs/GSCNN · loss.py:82

        neg_num = neg_index.sum()
        sum_num = pos_num + neg_num
        weight[pos_index] = neg_num*1.0 / sum_num
        weight[neg_index] = pos_num*1.0 / sum_num

        weight[ignore_index] = 0

        weight = torch.from_numpy(weight)
        weight = weight.cuda()
        loss = F.binary_cross_entropy_with_logits(log_p, target_t, weight, size_average=True)
        return loss

    def edge_attention(self, input, target, edge):
        n, c, h, w = input.size()
        filler = torch.ones_like(target) * 255
        return self.seg_loss(input, 
                             torch.where(edge.max(1)[0] > 0.8, target, filler))

View source record →